The present invention relates to detecting nodules in chest x-ray images, and more particularly, to nodule feature extraction in chest x-ray images.
Lung cancer is a leading cause of all cancer deaths, and the survival rate can be significantly improved if it is detected in an early stage. Chest x-ray radiographs are a popular and cost effective way to perform initial examination and screening for lung cancer. In particular, chest x-ray radiographs are used to detect lung nodules. Nodules are small masses of tissue, which can form on various organs in the human body, such as the lungs. However, lung cancer diagnosis using chest x-ray radiographs can be very difficult cognitively. Such diagnosis typically requires a radiologist to make decisions based on clues, which can be extremely difficult to decipher.
A number of techniques have been developed to improve the effectiveness and efficiency of lung nodule detection by a radiologist, including dual energy subtraction, image enhancement, and computer aided lung nodule detection. In particular, computer aided lung nodule detection techniques have been proposed to automatically detect lung nodules in chest x-ray images. Unfortunately, an automatic nodule detection technique that is able to effectively cope with variations of chest x-ray images including different image characteristics, different types of nodules, and different background structures is not yet available. Significant advancements are needed to make automatic nodule detection in chest x-ray radiographs a practically applicable technique.
Automatic nodule detection is typically performed by deriving discriminating features and designing classifiers that can effectively remove false positives from a list of candidates. Features that can effectively differentiate genuine nodules from similar background structures are difficult to extract. Only a limited number of effective feature extraction techniques have been proposed. Adaptive ring filtering based techniques evaluate the center pointed convergence of gradient vectors inside a region of interest surrounding a nodule. This depends only on the orientation distribution of the gradient vector and is independent of the intensity and contrast. The adaptive ring filtering based techniques can handle some weak nodules and capture some nodule structure information. However, a major disadvantage of such techniques is that they fail to incorporate sufficient nodule shape information when accumulating convergence evidence. Matching filtering based techniques apply filters with shapes similar to nodules to an input image to enhance the genuine nodules while attempting to suppress false positives and/or other background anatomical structures. Features are then extracted from the enhanced image. A number of matching filtering based techniques have been proposed, including Gaussian filters, learned (average) nodule shape filters, and Laplacian of Gaussian (LoG) filters. Matching filtering based techniques are able to remove a significant number of false positives, but have a limited capability in tolerating complex background structures. They also lack the capability to handle weak nodules.
An important issue in nodule feature extraction is the localization of an effective region of interest. For example, a snake model can be used to locate a nodule boundary for further feature extraction. However, nodule boundary localization is as difficult as nodule detection itself, if not more difficult. The snake model approach is ineffective in handling background structures and weak nodules. A blob feature extraction algorithm uses a set of robust criteria to establish a ring of interest and then uses a set of criteria to impose a robust validation within the ring of interest to accumulate evidence. This performs better than the features described above, but it is still a local feature based technique.
Conventional learning based techniques extract features (typically, simple features) at a given candidate position and/or nearby regions and feed them to a pre-trained classifier to determine whether a candidate is a false positive or not. There is no theoretic problem with learning based techniques as long as a large number of representative false positive samples and genuine nodule samples are available, adequate features are extracted, and the classifiers being used are capable of obtaining “true” decision boundaries. Practically, this is generally not feasible. It is not practical to assume that adequate features are readily available. Accordingly, relying solely on a learned classifier to dig out false positives may not be a feasible approach, since the features used by the learned classifier may not be sufficient in discriminating genuine nodules from false positives. In addition, there are rarely enough representative genuine nodule samples available to enable robust learning. The lack of discriminating capability of features and insufficient number of representative samples invariantly limit learning based techniques in practical applications.
Even though a significant amount of research has been concentrated on the issue of deriving effective features to differentiate nodules from false positives, the nodule feature extraction problem is far from solved. Therefore, an improved feature detection method, which overcomes the ineffectiveness of the current techniques, is desirable.